Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt

Age

Proportion of na: 0%

Gender

Proportion of na: 0%
Female Male
No 342 91
Yes 47 16

Prior Spine Surgery

Proportion of na: 0.2%
NA No Yes
No 1 274 158
Yes 0 31 32

1st surgeon: experience in ASD surgery

Proportion of na: 0%
2-10 years More than 10 years
No 109 324
Yes 5 58

ASA classification

Proportion of na: 0.6%

Decompression

Proportion of na: 0%
No Yes
No 240 193
Yes 38 25

Osteotomy

Proportion of na: 0%
No Yes
No 209 224
Yes 20 43

3CO

Proportion of na: 0%
No Yes
No 382 51
Yes 47 16

SPOs

Proportion of na: 0%

BMI_First Visit

Proportion of na: 1.8%

Tobacco use_First Visit

Proportion of na: 2.6%
Current Ex-User NA Non-User
No 74 84 11 264
Yes 11 14 2 36

Osteoporosis / osteopenia

Proportion of na: 0%
No Yes
No 340 93
Yes 54 9

Levels Previously operated - Lower

Proportion of na: 63.1%
C Iliac L NA S T
No 6 8 91 279 43 6
Yes 0 4 16 34 8 1

LGap

Proportion of na: 2.6%

RLL

Proportion of na: 2.6%

Number of Interbody Fusions

Proportion of na: 0%

Posterior Instrumented Fusion: Upper / Lower Levels

Proportion of na: 0%
Iliac+S L T
No 301 128 4
Yes 52 11 0

LL-Lordosis Difference

Proportion of na: 2.6%

Propensity Scores Common Support

## Loading required package: lattice
## 
## Attaching package: 'lattice'
## The following object is masked from 'package:boot':
## 
##     melanoma

Model Stats

  • Treatment proportion: 0.127
  • Model Type: elastic_net
  • Accuracy: 0.8982979
  • Params: alpha: 0.1 lambda: 0.0068096

Model Coefficients

Bootstraping replicas: 50

Average Treatment Effects - Quality Life

Outcome: 2Y. ODI - Score (%)
Distribution:
  0%  25%  50%  75% 100% 
 -67  -27  -14   -4   40 
Model Type: elastic_net 
RMSE: 17.5959997182535 
Params: alpha: 0.6142857
lambda: 0.4865523

ATE (Yes-No): 0 
Observational differences in treatment 0.206 (Yes-No) 

   Alif 2Y. ODI - Score (%)
1:  Yes           -14.61290
2:   No           -14.81887
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 2Y. SRS22 - SRS Subtotal score
Distribution:
    0%    25%    50%    75%   100% 
-0.950  0.215  0.700  1.160  3.050 
Model Type: elastic_net 
RMSE: 0.679982046849019 
Params: alpha: 0.1
lambda: 0.0018896

ATE (Yes-No): 0.0426602703645799 
Observational differences in treatment 0.18 (Yes-No) 

   Alif 2Y. SRS22 - SRS Subtotal score
1:  Yes                      0.8662500
2:   No                      0.6858672
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 2Y. SF36 - MCS
Distribution:
    0%    25%    50%    75%   100% 
-33.82  -3.69   3.72  12.94  39.74 
Model Type: elastic_net 
RMSE: 12.8461823109938 
Params: alpha: 0.55
lambda: 0.383285

ATE (Yes-No): 0 
Observational differences in treatment -0.242 (Yes-No) 

   Alif 2Y. SF36 - MCS
1:  Yes       3.941111
2:   No       4.183228
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 2Y. SF36 - PCS
Distribution:
    0%    25%    50%    75%   100% 
-18.94   0.72   6.64  12.42  38.99 
Model Type: elastic_net 
RMSE: 9.18209807459122 
Params: alpha: 0.6785714
lambda: 0.4118198

ATE (Yes-No): 0 
Observational differences in treatment 1.02 (Yes-No) 

   Alif 2Y. SF36 - PCS
1:  Yes       7.687037
2:   No       6.666929
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
     0%     25%     50%     75%    100% 
-72.000 -20.510 -10.000  -3.905  30.800 
Model Type: elastic_net 
RMSE: 14.0102112981178 
Params: alpha: 0.55
lambda: 0.8562396

ATE (Yes-No): 0 
Observational differences in treatment -1.878 (Yes-No) 

   Alif 6W. Major curve Cobb angle
1:  Yes                  -14.77412
2:   No                  -12.89612
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 1Y. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-64.00 -22.69 -10.36  -3.00  22.44 
Model Type: boosting 
RMSE: 14.4469288710997 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -1.66465923537967 
Observational differences in treatment -2.136 (Yes-No) 

   Alif 1Y. Major curve Cobb angle
1:  Yes                  -15.53875
2:   No                  -13.40321
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-23.631420  -6.000000  -1.411482   1.689195  18.000000 
Model Type: boosting 
RMSE: 5.94387830781916 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.7142857

ATE (Yes-No): -2.24813080830117 
Observational differences in treatment -2.954 (Yes-No) 

   Alif 6W. T1 Sagittal Tilt
1:  Yes            -4.965070
2:   No            -2.010682
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-30.098675  -5.808565  -2.187195   1.000000  20.000000 
Model Type: elastic_net 
RMSE: 5.83983563570657 
Params: alpha: 0.1
lambda: 0.0364041

ATE (Yes-No): -1.29985806546258 
Observational differences in treatment -2.397 (Yes-No) 

   Alif 1Y. T1 Sagittal Tilt
1:  Yes            -4.840364
2:   No            -2.442963
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 6W. Sagittal Balance
Distribution:
     0%     25%     50%     75%    100% 
-194.79  -69.00  -26.50    3.96  114.15 
Model Type: boosting 
RMSE: 53.413447232778 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5357143

ATE (Yes-No): -15.8937886710836 
Observational differences in treatment -33.252 (Yes-No) 

   Alif 6W. Sagittal Balance
1:  Yes            -63.58620
2:   No            -30.33438
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 1Y. Sagittal Balance
Distribution:
     0%     25%     50%     75%    100% 
-237.47  -67.07  -30.52    5.84  109.54 
Model Type: boosting 
RMSE: 52.9144031771406 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -20.3390487647309 
Observational differences in treatment -30.124 (Yes-No) 

   Alif 1Y. Sagittal Balance
1:  Yes            -59.74368
2:   No            -29.61955
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 6W. Global Tilt
Distribution:
    0%    25%    50%    75%   100% 
-68.62 -17.58  -6.00   1.52 149.41 
Model Type: boosting 
RMSE: 14.2043051559946 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.6428571

ATE (Yes-No): -4.8742459429936 
Observational differences in treatment -10.536 (Yes-No) 

   Alif 6W. Global Tilt
1:  Yes      -17.585294
2:   No       -7.049362
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 1Y. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-62.630 -16.000  -6.465   1.000  26.000 
Model Type: elastic_net 
RMSE: 12.0502873635078 
Params: alpha: 0.1
lambda: 0.2264674

ATE (Yes-No): -7.3543129635547 
Observational differences in treatment -10.582 (Yes-No) 

   Alif 1Y. Global Tilt
1:  Yes      -16.991026
2:   No       -6.409266
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-94.930 -24.045  -9.355   0.140  29.000 
Model Type: boosting 
RMSE: 15.7600356488601 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.8571429

ATE (Yes-No): -2.23254487160861 
Observational differences in treatment -10.943 (Yes-No) 

   Alif 6W. Lordosis (top of L1-S1)
1:  Yes                   -21.82942
2:   No                   -10.88661
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-94.63 -25.71  -9.00   0.00  23.38 
Model Type: boosting 
RMSE: 16.3496121974448 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5714286

ATE (Yes-No): -6.09500955049753 
Observational differences in treatment -13.732 (Yes-No) 

   Alif 1Y. Lordosis (top of L1-S1)
1:  Yes                   -24.84000
2:   No                   -11.10803
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 6W. LGap
Distribution:
       0%       25%       50%       75%      100% 
-96.12340 -24.28110  -9.06300   0.31715  78.92000 
Model Type: boosting 
RMSE: 17.3311857535895 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6428571

ATE (Yes-No): -1.61978158240683 
Observational differences in treatment -11.262 (Yes-No) 

   Alif  6W. LGap
1:  Yes -21.70294
2:   No -10.44091
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 1Y. LGap
Distribution:
      0%      25%      50%      75%     100% 
-94.8082 -25.2564  -9.0618   0.1456  22.0800 
Model Type: boosting 
RMSE: 16.2272726010664 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6071429

ATE (Yes-No): -4.84086064302004 
Observational differences in treatment -13.59 (Yes-No) 

   Alif  1Y. LGap
1:  Yes -24.56838
2:   No -10.97887
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 6W. Pelvic Tilt
Distribution:
    0%    25%    50%    75%   100% 
-36.41  -8.33  -2.42   2.00  14.42 
Model Type: boosting 
RMSE: 7.71581556709911 
Params: nrounds: 50.0
max_depth: 2
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5714286

ATE (Yes-No): -1.99650931844323 
Observational differences in treatment -6.345 (Yes-No) 

   Alif 6W. Pelvic Tilt
1:  Yes       -9.369216
2:   No       -3.024645
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
    0%    25%    50%    75%   100% 
-26.62  -7.10  -2.14   2.00  23.00 
Model Type: boosting 
RMSE: 7.0835730052974 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.7142857

ATE (Yes-No): -5.31459360141021 
Observational differences in treatment -5.775 (Yes-No) 

   Alif 1Y. Pelvic Tilt
1:  Yes       -8.026750
2:   No       -2.251544
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Average Treatment Effects - Complications

Outcome: complication
Distribution:
Proportion 
  0.296837 
Model Type: boosting 
Accuracy: 0.696017675036188 
Params: nrounds: 50.0
max_depth: 12
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): 0 
Observational differences in treatment 0.006 (Yes-No) 

   Alif complication
1:  Yes    0.3023256
2:   No    0.2961957
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Average Treatment Effects - Nombre Revisions

Outcome: reinterventions
Distribution:
  0%  25%  50%  75% 100% 
   0    0    0    1    6 
Model Type: boosting 

Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6785714

ATE (Yes-No): 0 
Observational differences in treatment 0.042 (Yes-No) 

   Alif reinterventions
1:   No       0.4701087
2:  Yes       0.5116279
`geom_smooth()` using method = 'loess' and formula 'y ~ x'